Much research has been done regarding the use of wireless sensor networks (WSNs) for structural health monitoring (SHM) on engineering structures. Most commonly, SHM is performed by analyzing structural vibrations. The problem with using vibrations for SHM is that vibrations can be affected by many factors, not just the condition of the structure (factors other than structural condition will herein be referred to as ‘extraneous factors’). Environmental factors such as temperature and humidity are examples of extraneous factors, as are factors related to the stimuli that produce the vibrations being monitored (e.g., the magnitude and placement of the stimuli affect the vibrations). These factors all contribute to the vibrations making it difficult to determine the contribution of structural condition to the vibrations.
The problem of environmental and operational variability associated with SHM based on vibrations has received a good amount of attention in research. Generally, approaches to the problem can be divided into two main categories: input-output and output-only sensor networks. With input-output approaches, in determining structural condition, measured values of extraneous parameters are input to regression models that describe the relationship between structural response and extraneous parameters. The disadvantage of this approach is that it is often difficult to establish good regression models. With output-only approaches, statistical methods are used to determine structural condition without measuring the extraneous factors.
Many different output-only methods have been proposed. One technique that has been investigated is the usage of factor analysis. Another popular approach is the usage of principal component analysis (PCA). González, A. G. and Fassois, S. D.: ‘Vibration-Based Statistical Damage Detection for Scale Wind Turbine Blades under Varying Environmental Conditions’, in, Surveillance 7, 2013, use PCA to perform SHM on wind turbines. Jin, S.-S. and Jung, H.-J.: ‘Vibration-Based Structural Health Monitoring Using Adaptive Statistical Method under Varying Environmental Condition’, in, Proceedings of SPIE Vol. 9064, 2014, use adaptive PCA to continually update the PCA model with new data to improve the performance of the system. Kamrujjaman Serker, N. H. M., Wu, Z., and Li, S.: ‘A Nonphysics-Based Approach for Vibration-Based Structural Health Monitoring under Changing Environmental Conditions’, Structural Health Monitoring, 2010, 9, (2), pp. 145-158, propose an output-only method involving regression analysis to compensate for environmental and operational variability. Gorinevsky, D. and Gordon, G.: ‘Spatio-Temporal Filter for Structural Health Monitoring’, in, American Control Conference, 2006, suggest using a spatio-temporal infinite impulse response to filter out the environmental and operational effects from damage estimates. Overall, these approaches are promising with regard to achieving immunity to environmental and stimuli-related variability but they tend to be computationally intensive.
More efficient output-only methods suitable for implementation in WSNs have also been proposed. Bocca, M., Toivola, J., Eriksson, L. M., et al.: ‘Structural Health Monitoring in Wireless Sensor Networks by the Embedded Goertzel Algorithm’, in, 2011 IEEE/ACM International Conference on Cyber-Physical Systems, use the efficient Goertzel algorithm to compute transmissibility. Lynch, J. P., Sundararajan, A., Law, K. H., et al.: ‘Embedding Damage Detection Algorithms in a Wireless Sensing Unit for Operational Power Efficiency’, Smart Materials and Structures, 2004, 13, (4), use an autoregressive process model to fit vibration data to sets of coefficients for different environmental and operational conditions. In some approaches, WSNs are used to perform modal analysis on structures. These approaches have the potential to achieve computational efficiency, however the extent to which they ameliorate the problem of environmental and operational variability is unclear. Also, regarding modal analysis in particular, an accurate synchronization of the motes in a network is required at the onset of data collection. Such an accurate synchronization may be expensive and definitely prohibitive in terms of energy consumption for WSNs requiring very frequent measurements. Also, the expectation of frequent and precise synchronization is normally unrealistic in harsh environments where structures are to be monitored.
Largely as a result of environmental and stimuli-related variability, WSNs have not seen widespread commercial usage in SHM applications. Most deployments have been of an experimental nature.